What is Apache Iceberg?

A data lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. A data transaction is a series of data exchanges that are conducted in a single operation. For example, when a customer withdraws money from a bank account, the bank conducts several data exchanges at the same time in one data transaction, including verifying the account has sufficient balance, verifying identity, and debiting the withdrawal from the account. A transactional data lake is a type of data lake that not only stores data at scale but also supports transactional operations and ensures that data is accurate, consistent, and allows you to track how data and data structure changes over time. These properties are collectively known as Atomicity, Consistency, Isolation, and Durability (ACID):

• Atomicity guarantees that each transaction is a single event that either succeeds or fails completely; there is no half-way status. 
• Consistency ensures that all data written is valid according to the defined rules of the data lake, ensuring that data is accurate and reliable. 
• Isolation ensures multiple transactions can occur at the same time without interfering with each other, ensuring that each transaction executes independently.
• Durability means that data is not lost or corrupted once a transaction is submitted. Data can be recovered in the event of a system failure, such as a power outage.

Apache Iceberg is suited for many data lake use cases, including:

• Data tables in data lakes that require frequent deletes, such as when enforcing data privacy laws.
• Data tables in data lake that require record level updates. This is helpful when your dataset requires frequent updates after data settles, for example, sales data that may change due to later events such as customer returns. Iceberg provides capabilities to update individual records without needing to republish the entire data set.
• Data tables in data lakes that have unpredictable changes, such as Slowly Changing Dimension (SCD) tables. An example of an SCD is a customer record table that includes name, location, and contact information which may change over time at unknown intervals.
• When transactions with the data lake requires guaranteed data validity, durability, and reliability, Apache Iceberg table formats can be deployed to ensure ACID transactions.
• When there is a need to go back in time to query historical versions of data to perform trend analysis, analyze changes to data over a period of time, or to restore or rollback to a previous version to correct issues.

Apache Iceberg offers a fast, efficient way to process large datasets at scale. It brings the following benefits:

1. Open source:  Apache Iceberg is an open source project, which means that it is free to use and can be customized to meet your specific needs. It also has an active community of developers who are continually improving and adding new features to the project. 
2. Scalability:  Apache Iceberg is designed to handle large datasets efficiently. It can partition and organize data across multiple nodes, which helps distribute the workload and speed up data processing. 
3. Performance: Apache Iceberg has a variety of features to optimize query performance, including columnar storage and compression techniques such as predicate push down and schema evolution. 
4. Flexibility:  Apache Iceberg allows you to change how your data is organized so that it can evolve over time without requiring you to rewrite your queries or rebuild your data structures. It also supports multiple data formats and data sources, which makes it easy to integrate with existing systems. 
5. Reliability:  Apache Iceberg ensures data consistency and reliability through its support for transactions. You can track how data changes over time and roll-back to historical versions to help you correct issues.